Latency-oriented pruning has also gained a growing amount of attention.  [1]} presents a framework for network compression under operational constraints, using Bayesian optimization to iteratively obtain compression hyperparameters that satisfies the constraints. Along the same line, NetAdapt [2]} iteratively prunes neurons across layers under the guidance of the empirical latency measurements on the targeting platform. While these methods push the frontier of latency constrained pruning, the hardware-incurred latency surface in fact offers much more potential under our enhanced pruning policy - as we show later, large rooms for improvements remain un-exploited and realizable.
